Commonly used video compression techniques, such as H.264 and VP8, as well as proposed techniques, such as H.265, HEVC and VP9, all generally use similar approaches and families of compression techniques. These compression techniques make a trade-off between the quality and the bit-rate of video data streams when providing inter-frame and intra-frame compression, but the amount of compression possible is largely dependent on the image resolution of each frame and the complexity of the image sequences. To illustrate the relationship between bitrate and resolution among other factors, it is possible to use an empirically-derived formula to show how the bitrate of a video encoded with, for example the H.264 compression technique, relates to the resolution of that video:bitrate∝Q×w×h×f×m  (1)where Q is a quality constant, w is the width of a video, h is the height of the video, f is the frame-rate of the video and m is the motion rank, where m∈{1, . . . , 4} and a higher m is used for fast-changing, hard-to-predict content. For images, f=1 and m=1.
Equation (1) illustrates the direct relationship between the bitrate and the quality constant Q. A typical value, for example, could be selected for Q based on published empirical data, but a significant amount of research is directed to optimizing a value for Q. Equation (1) also illustrates the direct relationship between the bitrate and the complexity of the image sequences, i.e. variable m. The aforementioned existing video codecs focus on spatial and temporal compression techniques. Proposed video compression techniques, such as H.265, HEVC and VP9, seek to improve upon the motion prediction and intra-frame compression of previous techniques, e.g., by optimizing a value for m.
Equation (1) further illustrates a direct relationship between the bitrate and the resolution of the video, i.e. variables w and h. In order to reduce the resolution of video, several techniques exist to downscale the resolution of video data to reduce the bitrate. As a result of the disadvantages of current compression approaches, existing network infrastructure and video streaming mechanisms are becoming increasingly inadequate to deliver large volumes of high quality video content to meet ever-growing consumer demands for this type of content. This can be of particular relevance in certain circumstances, for example in relation to live broadcasts, where bandwidth is often limited, and extensive processing and video compression cannot take place at the location of the live broadcast without a significant delay due to inadequate computing resources being available at the location. Advances in training of neural networks have helped to improve performance in a number of domains. However, neural networks have yet to surpass existing codecs in lossy image compression.